Crohn's disease is a chronic inflammatory bowel disease that has significant impact on the quality of life of an estimated 500,000 Americans. Recent studies have taught us that earlier, more intensive therapy with immunomodulator and anti-tumor necrosis factor (TNF) medications soon after diagnosis and before complications occur lead to better patient outcomes. However, these drugs have serious risks including life-threatening infections and an association with lymphoma. There are two major barriers in initiating these medications early in the disease course before patients have proven to have severe disease: (1) we need to choose appropriate patients who will need these drugs without over-treating those destined to have mild disease, and (2) based on fears of side-effects, patients are hesitant to use these medications until they believe their disease is severe enough to deserve them. To address these concerns, we have developed two tools. First, we have created novel statistical models using system dynamics analysis to predict an individual patient's Crohn's disease course based on clinical, serologic, and genetic factors. Second, we have produced a web-based decision aid to help patients understand and weigh the benefits and risks of available treatments for Crohn's disease. The proposed project will link these two products together to create a personalized shared decision making program explaining what is known about the risks and benefits of Crohn's disease therapy, and predicting how an individual patients' disease will behave over time with or without therapy. The goals of this project are to validate this currently available prediction model, to optimize the shared decision making program, and in the setting of a randomized controlled trial study the impact of these tools on patients' choices for therapy, adherence to chosen medications, decision quality, cost of care, and clinical outcomes of Crohn's disease. Our overall hypothesis is that this shared decision making program will help patients understand which treatments are right for them, which will lead to a higher acceptance of appropriate therapy, improved persistence with chosen therapy, lower costs and improved clinical outcomes. We expect that we will be able to operationalize this product by integrating it into the clinical encounter leading to improved patient communication and a change in the paradigm of how we communicate complex data to patients. This proposal responds to the AHRQ health information technology (IT) portfolio priority area of using health IT to improve health care decision making, and an NIH top priority to advance personalized medicine. Specifically, it focuses on communicating the best available individualized evidence to patients so that they can make an informed decision based on their expressed treatment preferences.